单细胞是最近两年比较火一项技术，以10X、BD等公司为国内主要仪器厂商，单细胞的分析方法和技术在这两年也是突飞猛进，在github看到一个汇总单细胞的项目，摘选了软件和方法部分分享给大家：

RNA-seq

  • anchor – [Python] – Find bimodal, unimodal, and multimodal features in your data
  • ascend – [R] – ascend is an R package comprised of fast, streamlined analysis functions optimized to address the statistical challenges of single cell RNA-seq. The package incorporates novel and established methods to provide a flexible framework to perform filtering, quality control, normalization, dimension reduction, clustering, differential expression and a wide-range of plotting.
  • BackSPIN – [Python] – Biclustering algorithm developed taking into account intrinsic features of single-cell RNA-seq experiments.
  • BASiCS – [R] – Bayesian Analysis of single-cell RNA-seq data. Estimates cell-specific normalization constants. Technical variability is quantified based on spike-in genes. The total variability of the expression counts is decomposed into technical and biological components. BASiCS can also identify genes with differential expression/over-dispersion between two or more groups of cells.
  • BatchEffectRemoval – [Python] – Removal of Batch Effects using Distribution-Matching Residual Networks
  • BEARscc – [R] – BEARscc makes use of ERCC spike-in measurements to model technical variance as a function of gene expression and technical dropout effects on lowly expressed genes.
  • bigSCale – [matlab] – An analytical framework for big-scale single cell data.
  • bonvoyage – [Python] – Transform percentage-based units into a 2d space to evaluate changes in distribution with both magnitude and direction.
  • BPSC – [R] – Beta-Poisson model for single-cell RNA-seq data analyses
  • CALISTA – [R] – CALISTA provides a user-friendly toolbox for the analysis of single cell expression data. CALISTA accomplishes three major tasks: 1) Identification of cell clusters in a cell population based on single-cell gene expression data, 2) Reconstruction of lineage progression and produce transition genes, and 3) Pseudotemporal ordering of cells along any given developmental paths in the lineage progression.
  • ccRemover – [R] – Removes the Cell-Cycle Effect from Single-Cell RNA-Sequencing Data. Identifying and removing the cell-cycle effect from single-cell RNA-Sequencing data.
  • celda – [R] – A suite of Bayesian hierarchical models and supporting functions to perform gene and cell clustering for count data generated by scRNA-seq platforms. This algorithm is an extension of the Latent Dirichlet Allocation (LDA) topic modeling framework that has been popular in text mining applications.
  • Cell_BLAST – [Python] – A BLAST-like toolkit for scRNA-seq data querying and automated annotation.
  • CellCNN – [Python] – Representation Learning for detection of phenotype-associated cell subsets
  • Cellity – [R] – Classification of low quality cells in scRNA-seq data using R
  • CellRanger – [Linux Binary] – Cell Ranger is a set of analysis pipelines that process Chromium single-cell RNA-seq output to align reads, generate gene-cell matrices and perform clustering and gene expression analysis. Software requires registration with 10xgenomics.
  • cellTree – [R] – Cell population analysis and visualization from single cell RNA-seq data using a Latent Dirichlet Allocation model.
  • clusterExperiment – [R] – Functions for running and comparing many different clusterings of single-cell sequencing data. Meant to work with SCONE and slingshot.
  • Clustergrammer – [Python, JavaScript] – Interative web-based heatmap for visualizing and analyzing high dimensional biological data, including single-cell RNA-seq. Clustergrammer can be used within a Jupyter notebook as an interative widget that can be shared using GitHub and NBviewer, see example notebook.
  • Clustergrammer2 – [Python, JavaScript] – Interative WebGL web-based heatmap for visualizing and analyzing single-cell high-dimensional and location-based biological data. Clustergrammer can be used within a Jupyter notebook as an interative widget that can be shared using GitHub and NBviewer, see case studies.
  • CountClust – [R] – Functions for fitting Grade-of-Membership models, also known as “Topic models”, to RNA-seq counts. These models generalize clustering methods to allow that each cell may belong to more than one cluster/topic.
  • cyclum – [python] – Cyclum is a novel AutoEncoder approach that characterizes circular trajectories in the high-dimensional gene expression space. Applying Cyclum to removing cell-cycle effects leads to substantially improved delineations of cell subpopulations, which is useful for establishing various cell atlases and studying tumor heterogeneity. bioRxiv
  • CytoGuide – [C++,D3] – CyteGuide: Visual Guidance for Hierarchical Single-Cell Analysis
  • DECENT – [R] – The unique features of scRNA-seq data have led to the development of novel methods for differential expression (DE) analysis. However, few of the existing DE methods for scRNA-seq data estimate the number of molecules pre-dropout and therefore do not explicitly distinguish technical and biological zeroes. We develop DECENT, a DE method for scRNA-seq data that adjusts for the imperfect capture efficiency by estimating the number of molecules pre-dropout.
  • DESCEND – [R] – DESCEND deconvolves the true gene expression distribution across cells for UMI scRNA-seq counts. It provides estimates of several distribution based statistics (five distribution measurements and the coefficients of covariates (such as batches or cell size)).
  • destiny – [R] – Diffusion maps are spectral method for non-linear dimension reduction introduced by Coifman et al.(2005). Diffusion maps are based on a distance metric (diffusion distance) which is conceptually relevant to how differentiating cells follow noisy diffusion-like dynamics, moving from a pluripotent state towards more differentiated states.
  • DensityPath – [.] – DensityPath: a level-set algorithm to visualize and reconstruct cell developmental trajectories for large-scale single-cell RNAseq data
  • DeLorean – [R] – Bayesian pseudotime estimation algorithm that uses Gaussian processes to model gene expression profiles and provides a full posterior for the pseudotimes.
  • dittoSeq – [R] – Bioconductor package offering user friendly visualization tools for single-cell and Bulk RNA Sequencing. Color blindness friendly by default; novice coder friendly; highly customizable and powerful enough to build publication-ready figures; universal in that it works directly with Seurat, SingleCellExperiment, and SummarizedExperiment objects and has import capabilities for edgeR DGElists.
  • dropClust – [R/Python] – Efficient clustering of ultra-large scRNA-seq data.
  • dropsim – [R] – Simulating droplet based scRNA-seq data.
  • dynamo – [Python] – Inclusive model of expression dynamics with scSLAM-seq and multiomics, vector field reconstruction and potential landscape mapping.
  • dynverse – [R] – A comparison of single-cell trajectory inference methods: towards more accurate and robust tools
  • ECLAIR – [python] – ECLAIR stands for Ensemble Clustering for Lineage Analysis, Inference and Robustness. Robust and scalable inference of cell lineages from gene expression data.
  • embeddr – [R] – Embeddr creates a reduced dimensional representation of the gene space using a high-variance gene correlation graph and laplacian eigenmaps. It then fits a smooth pseudotime trajectory using principal curves.
  • Falco – [AWS cloud] – Falco: A quick and flexible single-cell RNA-seq processing framework on the cloud.
  • FastProject – [Python] – Signature analysis on low-dimensional projections of single-cell expression data.
  • flotilla – [Python] – Reproducible machine learning analysis of gene expression and alternative splicing data
  • GPfates – [Python] – Model transcriptional cell fates as mixtures of Gaussian Processes
  • GPseudoClust – [Python] – Software that clusters genes for pseudotemporally ordered data and quantifies the uncertainty in cluster allocations arising from the uncertainty in the pseudotime ordering.
  • GiniClust – [Python/R] – GiniClust is a clustering method implemented in Python and R for detecting rare cell-types from large-scale single-cell gene expression data. GiniClust can be applied to datasets originating from different platforms, such as multiplex qPCR data, traditional single-cell RNAseq or newly emerging UMI-based single-cell RNAseq, e.g. inDrops and Drop-seq.
  • G2S3 – [R] – G2S3: a Sparse and Smooth Signal of Gene Graph-based imputation method for scRNA-seq data
  • HocusPocus – [R] – Basic PCA-based workflow for analysis and plotting of single cell RNA-seq data.
  • IA-SVA – [R] – Iteratively Adjusted Surrogate Variable Analysis (IA-SVA) is a statistical framework to uncover hidden sources of variation even when these sources are correlated with the biological variable of interest. IA-SVA provides a flexible methodology to i) identify a hidden factor for unwanted heterogeneity while adjusting for all known factors; ii) test the significance of the putative hidden factor for explaining the variation in the data; and iii), if significant, use the estimated factor as an additional known factor in the next iteration to uncover further hidden factors.
  • ICGS – [Python] – Iterative Clustering and Guide-gene Selection (Olsson et al. Nature 2016). Identify discrete, transitional and mixed-lineage states from diverse single-cell transcriptomics platforms. Integrated FASTQ pseudoalignment /quantification (Kallisto), differential expression, cell-type prediction and optional cell cycle exclusion analyses. Specialized methods for processing BAM and 10X Genomics spares matrix files. Associated single-cell splicing PSI methods (MultIPath-PSI). Apart of the AltAnalyze toolkit along with accompanying visualization methods (e.g., heatmap, t-SNE, SashimiPlots, network graphs). Easy-to-use graphical user and commandline interfaces.
  • inferCNV – [R] – Part of the TrinityCTAT (Trinity Cancer Transcriptome Analysis Toolkit). Provides tools for copy-number inference from single-cell RNA-seq data.
  • ivis – [Python or R] – Structure-preserving dimensionality reduction in single-cell datasets.
  • knn-smoothing – [python or R or matlab] – The algorithm is based on the observation that across protocols, the technical noise exhibited by UMI-filtered scRNA-Seq data closely follows Poisson statistics. Smoothing is performed by first identifying the nearest neighbors of each cell in a step-wise fashion, based on variance-stabilized and partially smoothed expression profiles, and then aggregating their transcript counts.
  • MAST – [R] – Model-based Analysis of Single-cell Transcriptomics (MAST) fits a two-part, generalized linear models that are specially adapted for bimodal and/or zero-inflated single cell gene expression data.
  • MERLoT – [R/python] – Reconstructing complex lineage trees from scRNA-seq data using MERLoT.
  • mfa – [R] – Probabilistic modeling of bifurcations in single-cell gene expression data using a Bayesian mixture of factor analyzers
  • K-Branches – [R] – The main idea behind the K-Branches method is to identify regions of interest (branching regions and tips) in differentiation trajectories of single cells. So far, K-Branches is intended to be used on the diffusion map representation of the data, so the user should either provide the data in diffusion map space or use the destiny package perform diffusion map dimensionality reduction.
  • M3Drop – [R] – Michaelis-Menten Modelling of Dropouts for scRNASeq.
  • MAGIC – [R, Python, MATLAB] – Markov Affinity-based Graph Imputation of Cells (MAGIC). A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data. On BioRviv and published in Cell.
  • MAST – [R] – Model-based Analysis of Single-cell Transcriptomics (MAST) fits a two-part, generalized linear models that are specially adapted for bimodal and/or zero-inflated single cell gene expression data
  • MIMOSCA – [python] – A repository for the design and analysis of pooled single cell RNA-seq perturbation experiments (Perturb-seq).
  • Monocle – [R] – Differential expression and time-series analysis for single-cell RNA-Seq.
  • NetDECODE – [R] – We develop an algorithm, called DECODE, to assess the extent of joint presence/absence of genes across different cells. We show that this network captures biologically-meaningful pathways, cell-type specific modules, and connectivity patterns characteristic of complex networks. We develop a model that uses this network to discriminate biological vs. technical zeros, by exploiting each gene’s local neighborhood. For non-biological zeros, we build a predictive model to impute the missing value using their most informative neighbors.
  • netSmooth – [R] – netSmooth is a network-diffusion based method that uses priors for the covariance structure of gene expression profiles on scRNA-seq experiments in order to smooth expression values. We demonstrate that netSmooth improves clustering results of scRNA-seq experiments from distinct cell populations, time-course experiments, and cancer genomics.
  • NetworkInference – [Julia] – Fast implementation of single-cell network inference algorithms: Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures
  • nimfa – [Python] – Nimfa is a Python scripting library which includes a number of published matrix factorization algorithms, initialization methods, quality and performance measures and facilitates the combination of these to produce new strategies. The library represents a unified and efficient interface to matrix factorization algorithms and methods.
  • novoSpaRc – [Python] – Predict locations of single cells in space by solely using single-cell RNA sequencing data. An existing reference database of marker genes is not required, but significantly enhances performance if available. bioRxiv.
  • OEFinder – [R] – Identify ordering effect genes in single cell RNA-seq data. OEFinder shiny impelemention depends on packages shiny, shinyFiles, gdata, and EBSeq.
  • OncoNEM – [R] – OncoNEM is a probabilistic method for inferring intra-tumor evolutionarylineage trees from somatic single nucleotide variants of single cells. OncoNEM identifies homogeneous cellularsubpopulations and infers their genotypes as well as a tree describing their evolutionary relationships.
  • ouija – [R] – A descriptive marker gene approach to single-cell pseudotime inference
  • ouijaflow – [python] – A descriptive marker gene approach to single-cell pseudotime inference
  • outrigger – [Python] – Outrigger is a program to calculate alternative splicing scores of RNA-Seq data based on junction reads and a de novo, custom annotation created with a graph database, especially made for single-cell analyses.
  • Palantir – [Python] – Characterization of cell fate probabilities in single-cell data with Palantir
  • pcaReduce – [R] – hierarchical clustering of single cell transcriptional profiles.
  • PHATE – Potential of Heat-diffusion for Affinity-based Transition Embedding – [Python, R, matlab] – PHATE is a tool for visualizing high dimensional single-cell data with natural progressions or trajectories. PHATE uses a novel conceptual framework for learning and visualizing the manifold inherent to biological systems in which smooth transitions mark the progressions of cells from one state to another.
  • PhenoPath – [R] – Single-cell pseudotime with heterogeneous genetic and environmental backgrounds, including Bayesian significance testing of iteractions.
  • powsimR – [R] – Power analysis is essential to optimize the design of RNA-seq experiments and to assess and compare the power to detect differentially expressed genes. PowsimR is a flexible tool to simulate and evaluate differential expression from bulk and especially single-cell RNA-seq data making it suitable for a priori and posterior power analyses.
  • pseudodynamics – [MATLAB] – Inferring population dynamics from single-cell RNA-sequencing time series data
  • psupertime – [R] – psupertime is an R package which uses single cell RNAseq data, where the cells have labels following a known sequence (e.g. a time series), to identify a small number of genes which place cells in that known order. It can be used for discovery of relevant genes, for exploration of unlabelled data, and assessment of one dataset with respect to the labels known for another dataset. – preprint
  • PyGMNormalize – [Python] – Python implementation of edgeR normalization method for count matrices.
  • rMATS – [Python] – RNA-Seq Multavariate Analysis of Transcript Splicing.
  • robustSingleCell – [R] – robustSingleCell is a pipeline designed to identify robust cell subpopulations using scRNAseq data and compare population compositions across tissues and experimental models via similarity analysis as described in Magen et al. (2019) bioRxiv.
  • SAVER – [R] – SAVER (Single-cell Analysis Via Expression Recovery) implements a regularized regression prediction and empirical Bayes method to recover the true gene expression profile in noisy and sparse single-cell RNA-seq data.
  • SAKE – [R] – Single-cell RNA-Seq Analysis and Clustering Evaluation.
  • SC3 – [R] – SC3 is a tool for the unsupervised clustering of cells from single cell RNA-Seq experiments.
  • SCALE – [R] – SCALE is a statistical framework for Single Cell ALlelic Expression analysis. SCALE estimates kinetic parameters that characterize the transcriptional bursting process at the allelic level, while accounting for technical bias.
  • Scanpy – [Python] – Scanpy provides computationally efficient tools that scale up to very large data sets and enables simple integraton of advanced machine learning algorithms.
  • scater – [R] – Scater places an emphasis on tools for quality control, visualisation and pre-processing of data before further downstream analysis, filling a useful niche between raw RNA-sequencing count or transcripts-per-million data and more focused downstream modelling tools such as monocle, scLVM, SCDE, edgeR, limma and so on.
  • SCCAF – [Python] Single Cell Clustering Assessment Framework (SCCAF) is a method for automated identification of putative cell types from single cell data by iteratively applying clustering and a machine learning approach. Putative cell type discovery from single-cell gene expression data
  • scDD – [R] – scDD (Single-Cell Differential Distributions) is a framework to identify genes with different expression patterns between biological groups of interest. In addition to traditional differential expression, it can detect differences that are more complex and subtle than a mean shift.
  • SCDE – [R] – Differential expression using error models and overdispersion-based identification of important gene sets.
  • SCDIFF – [Python, JavaScript] – SCDIFF is a single-cell trajectory inference method with interactive visualizations powered by D3.js. SCDIFF utilized the TF regulatory information to mitigate the impact of enormous single-cell RNA-seq noise (such as drop-out). With the TF regulatory information, SCDIFF is also able to predict the TFs (and their activation time), which drive the cells to different cell fates. Such predictive power has been experimentally validated.
  • SCell – [matlab] – SCell is an integrated software tool for quality filtering, normalization, feature selection, iterative dimensionality reduction, clustering and the estimation of gene-expression gradients from large ensembles of single-cell RNA-seq datasets. SCell is open source, and implemented with an intuitive graphical interface.
  • schist – [Python] – schist is a scanpy-compatible python library which implements Nested Stochastic Block Models to identify cell groups in single cell experiments.
  • Scillus – [R] – Scillus is an R wrapper package for enhanced processing and visualization of Seurat-based scRNA-seq data.
  • SCIMITAR – [Python] – Single Cell Inference of Morphing Trajectories and their Associated Regulation module (SCIMITAR) is a method for inferring biological properties from a pseudotemporal ordering. It can also be used to obtain progression-associated genes that vary along the trajectory, and genes that change their correlation structure over the trajectory; progression co-associated genes.
  • SCINA – [R] – A semi-supervised category identification and assignment tool.
  • scImpute – [R] – scImpute: Accurate And Robust Imputation For Single Cell RNA-Seq Data
  • Scirpy – [Python] – A Scanpy extension for analyzing single-cell T-cell receptor (TCR) sequencing data.
  • SCENIC – [R] – SCENIC is an R package to infer Gene Regulatory Networks and cell types from single-cell RNA-seq data. SCENIC: single-cell regulatory network inference and clustering
  • scVI – [python] – scVI is a ready-to-use scalable framework for the probabilistic representation and analysis of gene expression in single cells (batch correction, visualization, clustering, and differential expression). Deep generative modeling for single-cell transcriptomics
  • scvis – [python] – Interpretable dimensionality reduction of single cell transcriptome data with deep generative models
  • scLM – [R] – Automatic detection of consensus gene clusters across multiple single-cell datasets
  • scLVM – [R] – scLVM is a modelling framework for single-cell RNA-seq data that can be used to dissect the observed heterogeneity into different sources, thereby allowing for the correction of confounding sources of variation. scLVM was primarily designed to account for cell-cycle induced variations in single-cell RNA-seq data where cell cycle is the primary soure of variability.
  • scTDA – [Python] – scTDA is an object oriented python library for topological data analysis of high-throughput single-cell RNA-seq data. It includes tools for the preprocessing, analysis, and exploration of single-cell RNA-seq data based on topological representations.
  • scmap – [R] – scmap is a method for projecting cells from a scRNA-seq experiment on to the cell-types identified in a different experiment.
  • SCMarker – [R] – SCMarker is a method performing ab initial marker gene set selection from scRNA-seq data to achieve improved clustering/cell-typing results. SCMarker: ab initio marker selection for single cell transcriptome profiling.
  • SCnorm – [R] – A quantile regression based approach for robust normalization of single cell RNA-seq data.
  • SCODE – [R/Julia]- an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation
  • SCONE – [R] – SCONE (Single-Cell Overview of Normalized Expression), a package for single-cell RNA-seq data quality control (QC) and normalization. This data-driven framework uses summaries of expression data to assess the efficacy of normalization workflows.
  • SCORE – [R] – Enhancing single-cell cellular state inference by incorporating molecular network features.
  • SCORPIUS – [R] – An accurate and easy tool for performing linear trajectory inference on single cells using single-cell RNA sequencing data. In addition, SCORPIUS provides functions for discovering the most important genes with respect to the reconstructed trajectory, as well as nice visualisation tools. Cannoodt et al. (2016) doi:10.1101/079509.
  • SCOUP – [C++] – Uses probabilistic model based on the Ornstein-Uhlenbeck process to analyze single-cell expression data during differentiation.
  • scPLS – [C++, R] – A normalization method to remove unwanted variation using both control and target genes. It takes advantage of the fact that genes in a scRNAseq study often can be naturally classified into two sets: a control set of genes that are free of effects of the predictor variables and a target set of genes that are of primary interest. By modeling the two sets of genes jointly using the partial least squares regression, scPLS is capable of making full use of the data to improve the inference of confounding effects. https://www.nature.com/articles/s41598-017-13665-w
  • scran – [R] – This package implements a variety of low-level analyses of single-cell RNA-seq data. Methods are provided for normalization of cell-specific biases, pool-based norms to estimate size factors, assignment of cell cycle phase, and detection of highly variable and significantly correlated genes.
  • SCRL – [C++] – Network embedding-based representation learning for single cell RNA-seq data
  • scruff – [R] – An R package for preprocessing single cell RNA-seq (scRNA-seq) FASTQ reads generated by CEL-Seq and CEL-Seq2 protocols. It demultiplexes reads according to predetermined cell barcodes, aligns reads to reference genome using Rsubread aligner, and reports UMI (Unique Molecular Identifier) filtered count matrix ready for downstream analysis. It also provides functions to visualize the quality of data and the alignments of reads for individual cells.
  • scSVA – [R] – An R package for interactive two- and three-dimensional visualization and exploration of massive single-cell omics data (2-10^9 cells). scSVA supports interactive analytics in a cloud with containerized tools. It contains optimized implementation of diffusion maps and multi-threaded 3D force-directed layout (ForceAtlas2).
  • scTCRseq – [python] – Map T-cell receptor (TCR) repertoires from single cell RNAseq.
  • SCUBA – [matlab/R] – SCUBA stands for “Single-cell Clustering Using Bifurcation Analysis.” SCUBA is a novel computational method for extracting lineage relationships from single-cell gene expression data, and modeling the dynamic changes associated with cell differentiation.
  • scVelo – [Python] – scVelo is a scalable toolkit for RNA velocity analysis in single cells. It generalizes the concept of RNA velocity by relaxing previously made assumptions with a dynamical model. It allows to identify putative driver genes, infer a latent time, estimate reaction rates of transcription, splicing and degradation, and detect competing kinetics.
  • SEPA – [R] – SEPA provides convenient functions for users to assign genes into different gene expression patterns such as constant, monotone increasing and increasing then decreasing. SEPA then performs GO enrichment analysis to analysis the functional roles of genes with same or similar patterns.
  • Seurat – [R] – It contains easy-to-use implementations of commonly used analytical techniques, including the identification of highly variable genes, dimensionality reduction (PCA, ICA, t-SNE), standard unsupervised clustering algorithms (density clustering, hierarchical clustering, k-means), and the discovery of differentially expressed genes and markers.
  • SIMLR – [R, matlab] – SIMLR (Single-cell Interpretation via Multi-kernel LeaRning) learns an appropriate distance metric from the data for dimension reduction, clustering and visualization. SIMLR is capable of separating known subpopulations more accurately in single-cell data sets than do existing dimension reduction methods.
  • sincell – [R] – Existing computational approaches for the assessment of cell-state hierarchies from single-cell data might be formalized under a general workflow composed of i) a metric to assess cell-to-cell similarities (combined or not with a dimensionality reduction step), and ii) a graph-building algorithm (optionally making use of a cells-clustering step). Sincell R package implements a methodological toolbox allowing flexible workflows under such framework.
  • sincera – [R] – R-based pipeline for single-cell analysis including clustering and visualization.
  • SINCERITIES – [R/Matlab] – Inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles
  • SingleSplice – [R, perl, C++] – A tool for detecting biological variation in alternative splicing within a population of single cells. See Welch et al. 2016.
  • singlet – [Python] – Single cell RNA-Seq analysis with phenotypes.
  • SinQC – [R] – A Method and Tool to Control Single-cell RNA-seq Data Quality.
  • SISUA – [python] – In this study, we propose models based on the Bayesian generative approach, where protein quantification available as CITE-seq counts from the same cells are used to constrain the learning process, thus forming a semi-supervised model. The generative model is based on the deep variational autoencoder (VAE) neural network architecture. bioRxiv
  • SLICER – [R] – Selective Locally linear Inference of Cellular Expression Relationships (SLICER) algorithm for inferring cell trajectories.
  • slingshot – [R] – Functions for identifying and characterizing continuous developmental trajectories in single-cell sequencing data.
  • soupX – [R] – An R package for the estimation and removal of cell free mRNA contamination in droplet based single cell RNA-seq data. The problem this package attempts to solve is that all droplet based single cell RNA-seq experiments also capture ambient mRNAs present in the input solution along with cell specific mRNAs of interest.
  • SPADE – [R] – Visualization and cellular hierarchy inference of single-cell data using SPADE.
  • splatter – [R] – Splatter is a package for the simulation of single-cell RNA sequencing count data. It provides a simple interface for creating complex simulations that are reproducible and well-documented.
  • SPRING – SPRING is a collection of pre-processing scripts and a web browser-based tool for visualizing and interacting with high dimensional data. SPRING was developed for single cell RNA-Seq data but can be applied more generally. switchde – [R] – Differential expression analysis across pseudotime. Identify genes that exhibit switch-like up or down regulation along single-cell trajectories along with where in the trajectory the regulation occurs.
  • SWNE – [R] – Visualizing single-cell RNA-seq datasets with Similarity Weighted Nonnegative Embedding (SWNE)
  • TASC – [C++, python] – To account for cell-to-cell technical differences, we propose a statistical framework, TASC (Toolkit for Analysis of Single Cell RNA-seq), an empirical Bayes approach to reliably model the cell-specific dropout rates and amplification bias by use of external RNA spike-ins. TASC incorporates the technical parameters, which reflect cell-to-cell batch effects, into a hierarchical mixture model to estimate the biological variance of a gene and detect differentially expressed genes. More importantly, TASC is able to adjust for covariates to further eliminate confounding that may originate from cell size and cell cycle differences.
  • TASIC – [matlab] – TASIC is a new method for determining temporal trajectories, branching and cell assignments in single cell time series experiments. Unlike prior approaches TASIC uses on a probabilistic graphical model to integrate expression and time information making it more robust to noise and stochastic variations.
  • TooManyCells – [Haskell, CLI program] – Suite of graph-based tools for efficient, global, and unbiased identification and visualization of cell clades.
  • TopSLAM – Extracting and using probabilistic Waddington’s landscape recreation from single cell gene expression measurements.
  • TraCeR – [python] – Reconstruction of T-Cell receptor sequences from single-cell RNA-seq data.
  • TRAPeS – [python, C++] – TRAPeS (TCR Reconstruction Algorithm for Paired-End Single-cell), a software for reconstruction of T cell receptors (TCR) using short, paired-end single-cell RNA-sequencing.
  • trendsceek – [R] – Identification of spatial expression trends in single-cell gene expression data
  • TSCAN – [R] – Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis.
  • UNCURL – [Python] – Unsupervised and semi-supervised sampling effect removal for single-cell RNA-seq data.
  • VELOCYTO – [Python, R] – Estimating RNA velocity in single cell RNA sequencing datasets.
  • VIPER – [C++, R] – A fast and accurate tool to impute zero values in single-cell RNA sequencing studies to facilitate accurate transcriptome quantification at the single-cell level. VIPER is based on nonnegative sparse regression models and is capable of progressively inferring a sparse set of local neighborhood cells that are most predictive of the expression levels of the cell of interest for imputation. A key feature of VIPER is its ability to preserve gene expression variability across cells after imputation. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-018-1575-1VISION – [] – A tool for annotating the sources of variation in single cell RNA-seq data in an automated, unbiased and scalable manner. It produces an interactive, low latency and feature rich web-based report that can be easily shared amongst researchers.
  • ZIFA – [Python] – Zero-inflated dimensionality reduction algorithm for single-cell data.
  • zinbwaveZinger – [R] – We introduce a weighting strategy, based on a zero-inflated negative binomial model, that identifies excess zero counts and generates gene- and cell-specific weights to unlock bulk RNA-seq DE pipelines for zero-inflated data, boosting performance for scRNA-seq. https://doi.org/10.1186/s13059-018-1406-4
  • zUMIs – [R, perl, shell] – zUMIs: A fast and flexible pipeline to process RNA-seq data with UMIs.

Cell type identification and classification

  • cellassign – [R] – Automated, probabilistic assignment of scRNA-seq to known types. cellassign automatically assigns single-cell RNA-seq data to known cell types across thousands of cells accounting for patient and batch specific effects. Information about a priori known markers for cell types is provided as input to the model. cellassign then probabilistically assigns each cell to a cell type, removing subjective biases from typical unsupervised clustering workflows. bioRxiv
  • CHETAH – [R] – CHETAH: a selective, hierarchical cell type identification method for single-cell RNA sequencing. CHETAH (CHaracterization of cEll Types Aided by Hierarchical clustering) is an accurate cell type identification algorithm that is rapid and selective, including the possibility of intermediate or unassigned categories. Evidence for assignment is based on a classification tree of previously available scRNA-seq reference data and includes a confidence score based on the variance in gene expression per cell type. For cell types represented in the reference data, CHETAH’s accuracy is as good as existing methods. Its specificity is superior when cells of an unknown type are encountered, such as malignant cells in tumor samples which it pinpoints as intermediate or unassigned. bioRxiv
  • CIPR – [R] – (Cluster Identity PRedictor-pronounced cy-per). A Shiny web applet (and R-package) that helps annotating the cluster identities in single-cell RNA-sequencing (scRNA-seq) experiments. The algorithm compares gene expression signature of experimental clusters with known reference datasets. In addition to 7 reference datasets implemented in CIPR (2 from mouse and 5 from human), users can upload custom high-throughput reference data for specialized studies. The CIPR pipeline can be further tailored to different analytical contexts by excluding irrelevant reference subsets and low-variance reference genes from the analysis. The manuscript describing CIPR and comparing its performance against other similar software was published in BMC Bioinformatics. CIPR’s fast and computationally efficient calculations and graphical outputs will facilitate scRNA-seq analysis where the user wants to try different clustering parameters iteratively and examine the cluster identities. Source code for the Shiny and R-package implementations are available on GitHub.
  • Garnett – [R] – Garnett is a software package that faciliates automated cell type classification from single-cell expression data. Garnett works by taking single-cell data, along with a cell type definition (marker) file, and training a regression-based classifier. Once a classifier is trained for a tissue/sample type, it can be applied to classify future datasets from similar tissues. In addition to describing training and classifying functions, this website aims to be a repository of previously trained classifiers. Supervised Classification Enables Rapid Annotation of Cell Atlases
  • scANVI – [python] – single-cell ANnotation using Variational Inference (scANVI) is a semi-supervised variant of scVI designed to leverage any available cell state annotations — for instance when only one data set in a cohort is annotated, or when only a few cells in a single data set can be labeled using marker genes. Harmonization and Annotation of Single-cell Transcriptomics data with Deep Generative Models
  • singleCellNet – [R] – A near-universal step in the analysis of single cell RNA-Seq data is to hypothesize the identity of each cell. Often, this is achieved by finding cells that express combinations of marker genes that had previously been implicated as being cell-type specific, an approach that is not quantitative and does not explicitly take advantage of other single cell RNA-Seq studies. SingleCellNet, which addresses these issues and enables the classification of query single cell RNA-Seq data in comparison to reference single cell RNA-Seq data. bioRxiv
  • SingleR – [R] – SingleR leverages reference transcriptomic datasets of pure cell types to infer the cell of origin of each of the single cells independently. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nature Immunology (2019)
  • scCATCH – [R] – A single cell cluster-based annotation package from cluster marker genes identification to cluster annotation based on evidence-based score by matching the identified potential marker genes with known cell markers in tissue-specific cell taxonomy reference database (CellMatch) Automatic Annotation on Cell Types of Clusters from Single-Cell RNA Sequencing Data. iScience (2020)
  • DeepSort – A reference-free cell-type annotation tool for single-cell RNA-seq data using deep learning with a weighted graph neural network, which is learned based on the most comprehensive single-cell transcriptomics atlases involving 764,741 cells across 88 tissues of human and mouse. bioRxiv
  • ImmClassifier – [R,python,Docker] – A cell type annotation algorithm that employs a knowledge-based approach to annotating cells based on their underlying ontology and multitudes of previously-published data. By encoding immune cell hierarchy in a neural network, ImmClassifier is able to identify fine-grained cell types with high accuracy. By running in Docker the tool is platform-agnostic. bioRxiv

Doublet Identification

Copy number analysis

  • aneufinder – [R] – Bioconductor module for copy-number detection in single-cell whole genome sequencing (scWGS) and strand-seq data using a Hidden Markov Model or binary bisection method.
  • Ginkgo – [R, C] – Ginkgo is a web application for single-cell copy-number variation analysis.
  • HoneyBADGER – [R] – HoneyBADGER identifies and infers the presence of CNV and LOH events in single cells and reconstructs subclonal architecture using allele and expression information from single-cell RNA-sequencing data.

Variant calling

Epigenomics

  • BIRD – [C++/R] – BIRD is a tool for predicting chromatin accessibility and inferring regulatory element activities in single cells using scRNA-seq. Global prediction of chromatin accessibility using small-cell-number and single-cell RNA-seq
  • ChromA – [C++/Fortran] – Chromatin Accessibility Annotation Tool.
  • ChromVAR – [R] – Determine variations in chromatin accessibility across sets of annotations or peaks. Designed primarily for single-cell or sparse chromatin accessibility data, e.g. from scATAC-seq or sparse bulk ATAC or DNAse-seq experiments. BioRxiv
  • cisTopic – [R] – A probabilistic framework used to simultaneously discover coaccessible enhancers and stable cell states from sparse single-cell epigenomics data. https://www.nature.com/articles/s41592-019-0367-1
  • cicero – [R] – Predicts enhancer-gene pairs by co-accessibility. Also adapts monocle for single-cell ATAC-seq (clustering, trajectories, differential accessibility).
  • DeepCpg – DeepCpG is a deep neural network for predicting the methylation state of CpG dinucleotides in multiple cells. It allows to accurately impute incomplete DNA methylation profiles, to discover predictive sequence motifs, and to quantify the effect of sequence mutations.
  • Melissa – [R] – Melissa (MEthyLation Inference for Single cell Analysis), a Bayesian hierarchical method to quantify spatially-varying methylation profiles across genomic regions from single-cell bisulfite sequencing data (scBS-seq). Melissa clusters individual cells based on local methylation patterns, enabling the discovery of epigenetic differences and similarities among individual cells. The clustering also acts as an effective regularisation method for imputation of methylation on unassayed CpG sites, enabling transfer of information between individual cells. BioRxiv
  • SCALE – [python] – SCALE is a deeplearning tool combining GMM with VAE for single-cell ATAC-seq analysis (visualization, clustering, imputation, batch effect removal, downstream analysis for celltype-specific TFs). SCALE method for single-cell ATAC-seq analysis via latent feature extraction
  • SCATE – [R] – SCATE reconstructs activities of individual cis-regulatory elements (CREs) from single-cell ATAC-seq data by adaptively integrating information from co-activated CREs, similar cells, and publicly available regulome data. Single-cell ATAC-seq signal extraction and enhancement with SCATE
  • SCRAT – [R] – SCRAT provides essential tools for users to read in single-cell regolome data (ChIP-seq, ATAC-seq, DNase-seq) and summarize into different types of features. It also allows users to visualize the features, cluster samples and identify key features. Single-cell regulome data analysis by SCRAT
  • Signac – [R] – Signac is an extension of Seurat for the analysis, interpretation, and exploration of single-cell chromatin datasets.
  • AtacWorks – [python] – AtacWorks is a deep learning tool to denoise and identify accessible chromatin regions from low-coverage, low cell count, or low-quality ATAC-seq data. AtacWorks can denoise signal and identify peaks from rare cellular subtypes in a mixed population. Biorxiv

Multi-assay data integration

Rare cell detection

  • FiRE – Finder of rare entities (FiRE) helps identify rare cell types in voluminous single-cell datasets. Design of FiRE is inspired by the observation that rareness estimation of a particular data point is the flip side of measuring the density around it. In principle, FiRE uses the Sketching technique, a variant of locality sensitive hashing, to assign rareness score to every cell. Paper

Other applications

  • BASIC – [python] – BASIC is a semi-de novo assembly method to determine the full-length sequence of the BCR in single B cells from scRNA-seq data.
  • dropEst – [C++, R] – High-performance pipeline for initial analysis of droplet-based single-cell RNA-seq data (Drop-seq, inDrop, 10x and some others). Allows to estimate gene count matrix as well as diagnostic stats from fastq files with raw reads. Implements corrections for different noise sources.
  • dropSeqPipe – An automatic data handling pipeline for drop-seq/scrb-seq data. It runs from raw fastq.gz data until the final count matrix with QC plots along the way.
  • immunarch – [R] – R Package for Fast and Painless Exploration of Single-cell and Bulk T-cell/Antibody Immune Repertoires
  • MetaNeighbor – [R] – MetaNeighbor: a method to rapidly assess cell type identity using both functional and random gene sets
  • sasc – [C] – sasc stands for Simulated Annealing Single-Cell, an algorithm for performing phylogenetic analysis of single-cell cancer samples. Manuscript here.
  • scDataviz – [R] – scDataviz: single cell dataviz and downstream analyses, with a primary focus on flow and mass cytometry.
  • SCope – [python] – SCope is a fast visualization tool for large-scale and high dimensional scRNA-seq datasets. Publication here.
  • SiFit – [Java] – SiFit: A Method for Inferring Tumor Trees from Single-Cell Sequencing Data under Finite-site ModelsSinto – [python] – A toolkit for working with aligned single-cell reads. Includes functions to split BAM files by cell barcode, add cell barcodes as read tags, move cell barcode information to read groups, and create a scATAC-seq fragment file from a BAM file.
  • sircel – [python] – sircel (pronounced “circle”) separates reads in a fastq file based on barcode sequences that occur at known positions of reads. This is an essential first step in analyzing single-cell genomics data from experiments such as Drop-Seq. Barcode sequences often contain deletion and/or mismatch errors that arise during barcode synthesis and sequencing, and we have designed our barcode recovery approach with these issues in mind. In addition to identifying barcodes in an unbiased manner, sircel also quantifies their abundances. doi
  • Snakemake single-cell-rna-seq workflow – [python, R, snakemake] – An automated pipeline for single cell RNA-seq analysis.
  • VisCello – [R] – VisCello for C.elegans embryogenesis.
  • Wishbone – [python] – Wishbone is an algorithm to identify bifurcating developmental trajectories from single cell data. Wishbone can applied to both single cell RNA-seq and mass cytometry datasets.

Spatial transcriptomics

  • Starspace – [Python] – Defines a schema for gene or protein expression data containing spatially localized information. Converts data from a variety of assay types, including Spatial Transcriptomics, CODEX, In-situ Sequencing, MERFISH, osmFISH, and starMAP. Demonstrates how to visualize and interact with these data using common analysis packages, and convert the formats into loom and anndata objects, for downstream analysis in R and Python.
  • SpatialDe – [Python] – SpatialDE is a statistical test to identify genes with spatial patterns of expression variation from multiplexed imaging or spatial RNA-sequencing data.
  • NMFreg – [Python] – The method is proposed in Slide-seq paper and reconstructs expression of each Slide-seq bead as a weighted combination of metagene factors, each corresponding to the expression signature of an individual cell type, defined from scRNA-seq.

Tutorials and workflows

Web portals, apps, and databases

Web portals and databases

  • 10X Genomics datasets – 10x genomics public datasets, including 1.3M cell mouse brain dataset.
  • ASAP – Automated Single-cell Analysis Pipeline (deposited in BioRXiv on December 22, 2016).
  • cellBrowser – [Python, Javascript] Python pipeline and Javascript scatter plot library for single-cell datasets. Demo
  • CellView – CellView is an R Shiny web application that allows knowledge-based and hypothesis-driven exploration of processed single cell transcriptomic data. ref.
  • Cell_BLAST – A Web portal powered by Cell_BLAST (scRNA-seq querying tool) and ACA (scRNA-seq database).
  • conquer – A repository of consistently processed, analysis-ready single-cell RNA-seq data sets.
  • Curated Database of single-cell studies – Available as a tsv download. Over 500 single cell transcriptomics studies have been published to date. Many of these have data available, but the links between data, study, and systems studied can be hard to identify through literature search. This manuscript describes a nearly exhaustive and manually curated database of single cell transcriptomics studies with descriptions of what kind of data and what biological systems have been studied. bioRxiv.
  • D3E – Discrete Distributional Differential Expression (D3E) is a tool for identifying differentially-expressed genes, based on single-cell RNA-seq data.
  • EBI Single Cell Expression Atlas – The Single Cell Expression Atlas contains uniformly re-analysed single cell expression data across different species and provides interactive visualizations to explore that data.
  • Galaxy Single Cell Omics Workbench – dedicated Galaxy server for analyzing single cell data.
  • IRIS3 – IRIS3 (integrated cell-type-specific regulon inference server from single-cell RNA-Seq) is an easy-to-use server empowered by over 20 functionalities to support comprehensive interpretations and graphical visualizations of identified cell-type-specific regulons.
  • JingleBells – A repository of standardized single cell RNA-Seq datasets for analysis and visualization in IGV at the single cell level. Currently focused on immune cells (http://www.jimmunol.org/content/198/9/3375.long).
  • SCPortalen – SCPortalen: human and mouse single-cell centric database. ref
  • scRNA.seq.datasets – Collection of public scRNA-Seq datasets used by Hemberg Lab
  • scRNASeqDB – A database aggregating human single-cell RNA-seq datasets. ref
  • Single Cell Portal – The Single-Cell Portal was developed to facilitate open data and open science in Single-cell Genomics. The portal currently focuses on sharing scientific results interactively, and sharing associated datasets.
  • V-SVA – An R Shiny application for detecting and annotating hidden sources of variation in single cell RNA-seq data.
  • WOT – Waddington Optimal Transport (wot) uses time-course data to infer how the probability distribution of cells in gene-expression space evolves over time, by using the mathematical approach of optimal transport.

Interactive visualization and analysis

  • cellBrowser – [Python, Javascript] Python pipeline and Javascript scatter plot library for single-cell datasets. Demo
  • CellView – CellView is an R Shiny web application that allows knowledge-based and hypothesis-driven exploration of processed single cell transcriptomic data. ref.
  • Cerebro – [R] – Cerebro (cell report browser), a Shiny- and Electron-based standalone desktop application for macOS and Windows, which allows investigation and inspection of pre-processed single-cell transcriptomics data without requiring bioinformatic experience of the user. Through an interactive and intuitive graphical interface, users can i) explore similarities and heterogeneity between samples and cells clusters in 2D or 3D projections such as t-SNE or UMAP, ii) display the expression level of single genes or genes sets of interest, iii) browse tables of most expressed genes and marker genes for each sample and cluster. bioRxiv
  • CReSCENT – [R, Javascript, Python] – CReSCENT: CanceR Single Cell ExpressioN Toolkit (Mohanraj et al. 2020), is an intuitive and scalable web portal incorporating a containerized pipeline execution engine for standardized analysis of cancer scRNA-seq data and associated metadata. CReSCENT uses public data sets and preconfigured pipelines that are accessible to computational biology non-experts and are user-editable to allow optimization, comparison, and reanalysis for specific experiments. Users can also upload their own scRNA-seq data for analysis and results can be kept private or shared with other users.
  • FASTGenomics – [Python, R] – FASTGenomics is an online platform to share single-cell RNA sequencing data and analyses using reproducible workflows. Gene expression data can be shared meeting European data protection standards (GDPR). FASTGenomics enables the user to upload their own data and generate customized and reproducible workflows for the exploration and analysis of gene expression data (Scholz et al. 2018). Follow us on Twitter.
  • Ginkgo – [R, C] – Ginkgo is a web application for single-cell copy-number variation analysis and visualization.
  • Granatum – Granatum is a graphical single-cell RNA-seq (scRNA-seq) analysis pipeline for genomics scientists. Published in December 2017.
  • histoCAT [MATLAB]- Histology Topography Cytometry Analysis Toolbox (histoCAT) is a package to visualize and analyse highly multiplexed image cytometry data. histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data
  • iS-CellR – iS-CellR (Interactive platform for Single-cell RNAseq) is a web-based Shiny app that integrates the Seurat package with Shiny’s reactive programming framework to provide comprhensive analysis and interactive visualization of single-cell RNAseq data. Paper
  • iSEE – [R] – iSEE, interactive SummarizedExperiment Explorer. The iSEE package aims to provide an interactive user interface for exploring data in objects derived from the SummarizedExperiment class. Particular focus will be given to single-cell data in the SingleCellExperiment derived class. The interface is implemented with RStudio’s Shiny, with a multi-panel setup for ease of navigation. Features include: dynamically linked charts, support for reproducibility by recording the exact code for every output, as well as guided tours to learn step-by-step the salient features of the user interface and of the data. A demo instance of the app is available at this address: http://shiny.imbei.uni-mainz.de:3838/iSEE.
  • NASQAR – Nucleic Acid SeQuence Analysis Resource, a web-based platform that provides an intuitive interface for popular tools (like DESeq2, Seurat, and others) to perform standard downstream analysis workflows for RNAseq data. The portal hosts a number of R Shiny apps.
  • PIVOT – Platform for Interactive analysis and Visualization Of Transcriptomics data. ref
  • scClustViz – An interactive R Shiny tool for visualizing single-cell RNAseq clustering results from common analysis pipelines (SingleCellExperiment or Seurat, currently). Its main goal is two-fold: A: to help select a biologically appropriate resolution or K from clustering results by assessing differential expression between the resulting clusters; and B: help annotate cell types and identify marker genes. See the demo app here! scClustViz can also be used to generate R data packages for sharing published data – see the website for details and a list of published datasets.
  • SeuratWizard – a web-based (wizard style) interactive R Shiny application to perform guided single-cell RNA-seq data analysis and clustering. demo
  • SeuratV3Wizard – a web-based (wizard style) interactive R Shiny application to perform guided single-cell RNA-seq data analysis and clustering based on Seurat v3. demo
  • ShinyCortex – a resource that brings together data from recent scRNA-seq studies of the developing cortex for further analysis. ShinyCortex is based in R and displays recently published scRNA-seq data from the human and mouse cortex in a comprehensible, dynamic and accessible way, suitable for data exploration by biologists. paper
  • singleCellTK – The singleCellTK is an R/Shiny package and GUI for analyzing and visualizing scRNA-Seq through a web interface. Analysis modules include data summary and filtering, dimensionality reduction and clustering, batch correction, differential expression analysis, pathway activity analysis, and power analysis.
  • STREAM – STREAM is an interactive computational pipeline for reconstructing complex celluar developmental trajectories from sc-qPCR, scRNA-seq or scATAC-seq data. preprint.
  • V-SVA – An R Shiny application for detecting and annotating hidden sources of variation in single cell RNA-seq data.

参考资料：

1.https://github.com/seandavi/awesome-single-cell

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